Applying Variant Variable Regularized Logistic Regression for Modeling Software Defect Predictor
Bibliographic record
Abstract
Empirical studies on software defect prediction models have come up with various predictors.In this study we examined variable regularized factors in conjunction with Logistic regression.Our work was built on eight public NASA datasets commonly used in this field.We used one of the datasets for our learning classification out of which we selected the regularization factor with the best predictor model; we then used the same regularization factor to classify the other seven datasets.Our proposed algorithm Variant Variable Regularized Logistic Regression (VVRLR) and modified VVRLR; were then used in the following metrics to measure the effectiveness of our predictor model: accuracy, precision, recall and F-Measure for each dataset.We measured above metrics using three Weka models, namely: BayesianLogisticRegression, NaiveBayes and Simple Logistic and then compared these results with VVRLR.VRLR and modified VVRLR outperformed the weka algorithms per our metric measurements.The VVRLR produced the best accuracy of 100.00%, and an average accuracy of 91.65 %; we had an individual highest precision of 100.00%, highest individual recall of 100.00% and F-measure of 100.00% as the overall best with an average value of 76.41% was recorded by VVRLR for some datasets used in our experiments.Our proposed modified VVRLR and variant VVRLR algorithms for F-measures outperformed the three weka algorithms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".